The recently published Progressive-X [1]} discussed that the first, instance generation, step of the mentioned methods leads to a number of issues, , the instances are generated blindly, having no information about the data at hand.
This approach severely restricts the out-of-the-box applicability of such techniques since the user either has to consider the worst-case scenario and, thus, generate an unnecessarily high number of instances; or requires some rule of thumb, , to generate twice the point number hypotheses that provides no guarantees of finding the sought instances.
Prog-X [1]} approaches the problem via interleaving the model proposal and optimization steps.
The idea was recently combined with deep-learning-based sampling in [3]} which was shown to further improve the accuracy.
